Parametrically guided nonparametric density and hazard estimation with censored data

Abstract

The parametrically guided kernel smoother is a promising nonparametric estimation approach that aims to reduce the bias of the classical kernel density estimator without increasing its variance. Theoretically, the estimator is unbiased if a correct parametric guide is used, which can never be achieved by the classical kernel estimator even with an optimal bandwidth. The estimator is generalized to the censored data case and used for density and hazard function estimation. The asymptotic properties of the proposed estimators are established and their performance is evaluated via finite sample simulations. The method is also applied to data coming from a study where the interest is in the time to return to drug use.